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Prediction model for mechanical properties of hot-rolled strips by deep learning

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Journal of Iron and Steel Research International 2020年第9期27卷 1045-1053页

作者： Wei-gang Li Lu Xie Yun-tao Zhao Zi-xiang Li Wen-bo Wang Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081HubeiChina Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081HubeiChina Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan University of Science and TechnologyWuhan 430081HubeiChina

The prediction of the mechanical properties of hot-rolled strips is a very complex,highly dimensional and nonlinear problem,and the published models might lack reliability,practicability and ***,a new model was proposed for predicting the mechanical properties of hot-rolled strips by deep ***,the one-dimensional numerical data were transformed into two-dimensional data for expressing the complex interaction between the influencing ***,a new convolutional network was proposed to establish the prediction model of tensile strength of hot-rolled strips,and an improved inception module was introduced into this network to abstract features from different *** comparative experiments were carried out to find the optimal network structure and its ***,the prediction experiments were carried out on different models to evaluate the performance of the new convolutional network,which includes the stepwise regression,ridge regression,support vector machine,random forest,shallow neural network,Bayesian neural network,deep feed-forward network and improved LeNet-5 convolutional neural *** results show that the proposed convolutional network has better prediction accuracy of the mechanical properties of hot-rolled strips compared with other models.

关键词： Deep learning Convolutional neural network Mechanical property Hot-rolled strip

Atf-Bss: Adaptive Time-Frequency Blind Source Separation for High Reverberation and Echo Environments

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SSRN 2023年

作者： Xie, Yuan Zou, Tao Chen, Ci Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation with high reverberation and echoes is an open and extremely challenging issue. Because the room reverberation and echoes have an important influence on acoustics quality, bringing great difficulty to the independent source separation. In the paper, an adaptive time-frequency blind source separation (ATF-BSS) algorithm is designed to deal with the high reverberation and echoes, which can adaptively attenuate the audible echoes and enhance the harmonic structure of the real-world audio signals, achieving excellent separation performance. Firstly, an equalizer network is designed for the room impulse response to weakening audible echoes under the high reverberation and echo environments without changing the perceived timbre. Furthermore, an adaptive time-frequency Wiener-like masking is presented to enhance the dominant signal and attenuate the other components having less energy than the dominant signal. To the best of our knowledge, this is the first attempt to incorporate the advantages of an equalizer network for removing or weakening audible echoes and adaptive time-frequency masking to solve blind separation with high reverberation and echoes. Experimental results on the public audio datasets demonstrate that the proposed ATF-BSS algorithm improves signal intelligibility and achieves much better separation performance than the state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Reverberation

Atf-Bss: Adaptive Time-Frequency Blind Source Separation for High Reverberation and Echo Environments

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SSRN 2023年

Blind source separation with high reverberation and echoes is an open and extremely challenging issue. Room reverberation and echoes have an important influence on acoustics quality, causing great difficulty in independent source separation. In the paper, an adaptive time-frequency blind source separation algorithm (ATF-BSS) is designed to deal with high reverberation and echoes, which can adaptively attenuate audible echoes and enhance the harmonic structure of real-world audio signals, achieving excellent separation performance. Firstly, an equalizer network is designed for the room impulse response to weakening audible echoes under high reverberation and echo environments without changing the perceived timbre. Furthermore, an adaptive time-frequency Wiener-like masking is presented to enhance the dominant signal and attenuate the other components that have less energy than the dominant signal. To the best of our knowledge, this is the first attempt to incorporate the advantages of an equalizer network for removing or weakening audible echoes and adaptive time-frequency masking to solve blind separation with high reverberation and echoes. Experimental results on public audio datasets demonstrate that the proposed ATF-BSS algorithm improves signal intelligibility and achieves much better separation performance than state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Reverberation

Room Impulse Response Reshaping-Based Blind Source Separation in an Underdetermined Reverberant Environment

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SSRN 2023年

作者： Xie, Yuan Zou, Tao Yang, Junjie Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation in an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation-maximization algorithm. However, it is limited in a high reverberation environment, resulting in bad or even invalid separation performance. To get rid of this restriction, a room impulse response reshaping-based blind source separation algorithm is designed to solve the signal separation problem in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo for the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation-maximization algorithm is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than the popular expectation-maximization methods. © 2023, The Authors. All rights reserved.

关键词： Blind source separation

Atf-Bss: Adaptive Time-Frequency Blind Source Separation for High Reverberation and Echo Environments

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SSRN 2023年

Blind source separation with high reverberation and echoes is an open and extremely challenging issue. Room reverberation and echoes have an important influence on acoustics quality, causing great difficulty in independent source separation. In the paper, an adaptive time-frequency blind source separation algorithm (ATF-BSS) is designed to deal with high reverberation and echoes, which can adaptively attenuate audible echoes and enhance the harmonic structure of real-world audio signals, achieving excellent separation performance. Firstly, an equalizer network is designed for the room impulse response to weakening audible echoes under high reverberation and echo environments without changing the perceived timbre. Furthermore, an adaptive time-frequency Wiener-like masking is presented to enhance the dominant signal and attenuate the other components that have less energy than the dominant signal. ATF-BSS algorithm incorporates the advantages of an equalizer network for removing or weakening audible echoes and adaptive time-frequency masking for high reverberation and echoes scene, improving the performance of blind separation. Experimental results on public audio datasets demonstrate that the proposed ATF-BSS algorithm improves signal intelligibility and achieves much better separation performance than state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Reverberation

Two Feature Fusion Network Based on Efficient Deep Residual Shrinkage Spatial-Temporal Graph Convolution for Emotion Recognition from Gaits

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Two Feature Fusion Network Based on Efficient Deep Residual ...

第34届中国控制与决策会议

作者： Xiaoqing Liu Sen Zhang Wendong Xiao School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Shunde Graduate School University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging

Emotion Recognition plays a key role in human social *** current methods choose facial information for emotion *** facial information is easily limited by distance in large *** information can break this limitation,but the amount of publicly available data is *** paper proposes two feature fusion network based on efficient deep residual shrinkage spatial-temporal graph convolution to achieve automatic emotion recognition from gaits in large *** chebyshev polynomial approximation graph convolution is combined with efficient deep residual shrinkage network to obtain efficient deep residual shrinkage spatial-temporal graph convolution network to extract deep *** sixteen temporal affective features extracted from gaits are used as input of efficient deep residual shrinkage network to extract new affective *** the deep features is spliced with new affective features to divided emotion into happy,angry,sad and *** performed a ten-flod cross-validation experiment on the E-gait real *** highest recognition accuracy is 0.87,with an average of *** results show that the proposed model is effective in dealing with emotion recognition promblem from gait in fewer sample situation.

关键词： emotion recognition from gaits chebyshev polynomial approximation deep residual shrinkage Network efficient channel attention mechanism

Stochastic configuration networks with robust supervised least squares regression

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Neurocomputing 2025年 647卷

作者： Fei Chu Zihang Sun Yu Wang Yong Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou 221116 China The State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing 100160 China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou 221116 China

Stochastic Configuration Networks (SCNs) are widely used in regression modeling to fit data distributions due to their fast convergence and powerful learning capabilities. However, in practical industrial applications, labeled outliers in the dataset inevitably appear, which can destroy the internal structure and connection of the data, and not only seriously reduce the prediction accuracy of SCNs, but may also increase the risk of overfitting. To cope with this problem, this paper proposes a new algorithm - Robust Supervised Stochastic Configuration Networks (RSSCNs). In this paper, firstly, the loss function is constructed based on the concave function of the error, which extends the applicability of the robust supervised least squares regression. Second, robust supervised least squares regression is introduced into the SCN algorithm for the first time, which enhances the robustness of the model and reduces the risk of model overfitting. Finally, the inequality constraint of the SCN algorithm is reconstructed according to the loss function, which optimizes the parameter selection and accelerates the convergence of the model. In the experimental section, RSSCNs are applied to a function fitting experiment, several benchmark datasets, and an industrial dataset from a dense medium coal preparation process, and the performance is compared with other algorithms. The experimental results show that for these datasets, RSSCNs perform favorably in terms of accuracy and robustness, validating the effectiveness of the algorithm.

关键词： Least squares regression Randomized algorithms Robust regression Stochastic configuration network

The Variation Characteristics of the Spread-F Occurrences at Xi'an in Northwest China

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The Variation Characteristics of the Spread-F Occurrences at...

International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Ning Wang Q Junsuo Qing Wu Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an University of Posts and Telecommunications School of Automation Xi'an University of Posts and Telecommunications Xi'an Shaanxi China

ISBN: (纸本)9781665418133

Ionospheric spread-F is a subject widely studied by scholars at home and abroad in recent decades. Its occurrence probability is affected by solar activity, geomagnetic activity, season and other factors. Due to its geographical location, Xi'an has become one of the most representative cities in Northwest China. Therefore, studying the variation characteristics of spread-F occurrence probabilities in Xi'an has important strategic significance for ionospheric research in China. This paper introduces the ionoapheric observation data of Xi”an (34.13°N, 108.83°E) in the mid-latitude of China, and studies the occurrence rate and monthly and seasonal variation characteristics of spread-F during 2011. FSF and RSF frequently occurred during summer at Xi”an. The results show that in the middle latitude, the start time of spread-F is about 21 LT and continues to 05 LT. Both the higher occurrences of FSF and RSF are mainly appeared at 22, Oland 04 local times. The probability of RSF occurring at some time in winter is higher than that in spring and autumn. Although some of the results of this paper are slightly different from the previous research results, most of the results indirectly verify the previous research results. All these results are of great significance for us to understand the variation characteristics of ionospheric irregularities in China and improve the ability of space weather prediction. However, the great difference of occurrences in Xi'an station is striking and worth further studying.

关键词： Antenna theory Urban areas Weather forecasting Springs

Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data control

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Global Consensus for Heterogeneous Saturated Multi-Agent Sys...

IEEE Conference on Decision and control

作者： Juan Qian Xiaoling Wang Daniele Astolfi Housheng Su Guo-Ping Jiang College of Automation and the College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Center for IOT Intelligent Robots(IOTRobot) Nanjing China CNRS LAGEPP UMR 5007 43 boulevard du 11 novembre 1918 Université Claude Bernard Lyon 1 Villeurbanne France Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the global consensus problem for multi-input multi-output saturated systems within a sampled-data framework, aiming to advance global consensus, manage heterogeneous actuator saturation across components, and preserve distributed characteristics by using sampled-data feedback. We propose a distributed control algorithm that incorporates a redesigned saturation function, represented as decentralized dynamic saturation levels. These levels for each agent’s dimensions are autonomously updated through an adaptive strategy, which mitigates heterogeneous saturation by carefully selecting constant and time-varying saturation parameters in it. Lyapunov analysis proves that global consensus can be achieved under the proposed control law, provided the sampling periods of all agents remain below a calculated threshold. An example is given to demonstrate the effectiveness of this approach.

关键词： Actuators Upper bound Heuristic algorithms Decentralized control Multi-agent systems